Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy

PURPOSE: Early clinical recognition of sepsis can be challenging. With the advancement of machine learning, promising real-time models to predict sepsis have emerged. We assessed their performance by carrying out a systematic review and meta-analysis. METHODS: A systematic search was performed in Pu...

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Detalles Bibliográficos
Autores principales: Fleuren, Lucas M., Klausch, Thomas L. T., Zwager, Charlotte L., Schoonmade, Linda J., Guo, Tingjie, Roggeveen, Luca F., Swart, Eleonora L., Girbes, Armand R. J., Thoral, Patrick, Ercole, Ari, Hoogendoorn, Mark, Elbers, Paul W. G.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2020
Materias:
Systematic Review
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7067741/
https://www.ncbi.nlm.nih.gov/pubmed/31965266
http://dx.doi.org/10.1007/s00134-019-05872-y

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7067741/
https://www.ncbi.nlm.nih.gov/pubmed/31965266
http://dx.doi.org/10.1007/s00134-019-05872-y

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